Add MiniGPT-4 example

.gitignore

@@ -40,7 +40,7 @@ models/*
 /vdot
 /server
 /Pipfile
-/embd_input_test
+/embd-input-test
 /libllama.so
 build-info.h
 arm_neon.h

examples/embd-input/.gitignore

@@ -0,0 +1,4 @@
+PandaGPT
+MiniGPT-4
+*.pth
+

examples/embd-input/README.md

@@ -1,17 +1,17 @@
 ### Examples for input embedding directly
 
 ## Requirement
-build  `libembd_input.so`
+build  `libembdinput.so`
 run the following comman in main dir (../../).
 ```
 make
 ```
 
-## LLAVA example  (llava.py)
+## [LLaVA](https://github.com/haotian-liu/LLaVA/) example  (llava.py)
 
-1. obtian llava model (following https://github.com/haotian-liu/LLaVA/ , use https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/)
+1. Obtian LLaVA model (following https://github.com/haotian-liu/LLaVA/ , use https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/).
-2. convert it to ggml format
+2. Convert it to ggml format.
-3. llava_projection.pth is [pytorch_model-00003-of-00003.bin](https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/blob/main/pytorch_model-00003-of-00003.bin)
+3. `llava_projection.pth` is [pytorch_model-00003-of-00003.bin](https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/blob/main/pytorch_model-00003-of-00003.bin).
 
 ```
 import torch
@@ -23,10 +23,12 @@ dic = torch.load(bin_path)
 used_key = ["model.mm_projector.weight","model.mm_projector.bias"]
 torch.save({k: dic[k] for k in used_key}, pth_path)
 ```
+4. Check the path of LLaVA model and `llava_projection.pth` in `llava.py`.
 
-## PandaGPT example (panda_gpt.py)
 
-1. Obtian PandaGPT lora model. Rename the file to `adapter_model.bin`. Use [convert-lora-to-ggml.py](../../convert-lora-to-ggml.py) to convert it to ggml format.
+## [PandaGPT](https://github.com/yxuansu/PandaGPT) example (panda_gpt.py)
+
+1. Obtian PandaGPT lora model from https://github.com/yxuansu/PandaGPT. Rename the file to `adapter_model.bin`. Use [convert-lora-to-ggml.py](../../convert-lora-to-ggml.py) to convert it to ggml format.
 The `adapter_config.json` is
 ```
 {
@@ -40,8 +42,22 @@ The `adapter_config.json` is
   "target_modules": ["q_proj", "k_proj", "v_proj", "o_proj"]
 }
 ```
-2. papare the `vicuna` v0 model.
+2. Papare the `vicuna` v0 model.
-3. obtain the [ImageBind](https://dl.fbaipublicfiles.com/imagebind/imagebind_huge.pth) model.
+3. Obtain the [ImageBind](https://dl.fbaipublicfiles.com/imagebind/imagebind_huge.pth) model.
 4. Clone the PandaGPT source.
-5. check the path of PandaGPT source, ImageBind model, lora model and vicuna model in panda_gpt.py.
+```
+git clone https://github.com/yxuansu/PandaGPT
+```
+5. Install the requirement of PandaGPT.
+6. Check the path of PandaGPT source, ImageBind model, lora model and vicuna model in panda_gpt.py.
 
+## [MiniGPT-4](https://github.com/Vision-CAIR/MiniGPT-4/) example (minigpt4.py)
+
+1. Obtain MiniGPT-4 model from https://github.com/Vision-CAIR/MiniGPT-4/ and put it in `embd-input`.
+2. Clone the MiniGPT-4 source.
+```
+git clone https://github.com/Vision-CAIR/MiniGPT-4/
+```
+3. Install the requirement of PandaGPT.
+4. Papare the `vicuna` v0 model.
+5. Check the path of MiniGPT-4 source, MiniGPT-4 model and vicuna model in `minigpt4.py`.

examples/embd-input/minigpt4.py

@@ -0,0 +1,128 @@
+import sys
+import os
+sys.path.insert(0, os.path.dirname(__file__))
+from embd_input import MyModel
+import numpy as np
+from torch import nn
+import torch
+from PIL import Image
+
+minigpt4_path = os.path.join(os.path.dirname(__file__), "MiniGPT-4")
+sys.path.insert(0, minigpt4_path)
+from minigpt4.models.blip2 import Blip2Base
+from minigpt4.processors.blip_processors import Blip2ImageEvalProcessor
+
+
+class MiniGPT4(Blip2Base):
+    """
+    MiniGPT4 model from https://github.com/Vision-CAIR/MiniGPT-4
+    """
+    def __init__(self,
+        args,
+        vit_model="eva_clip_g",
+        q_former_model="https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained_flant5xxl.pth",
+        img_size=224,
+        drop_path_rate=0,
+        use_grad_checkpoint=False,
+        vit_precision="fp32",
+        freeze_vit=True,
+        freeze_qformer=True,
+        num_query_token=32,
+        llama_model="",
+        prompt_path="",
+        prompt_template="",
+        max_txt_len=32,
+        end_sym='\n',
+        low_resource=False,  # use 8 bit and put vit in cpu
+        device_8bit=0
+    ):
+        super().__init__()
+        self.img_size = img_size
+        self.low_resource = low_resource
+        self.preprocessor = Blip2ImageEvalProcessor(img_size)
+
+        print('Loading VIT')
+        self.visual_encoder, self.ln_vision = self.init_vision_encoder(
+            vit_model, img_size, drop_path_rate, use_grad_checkpoint, vit_precision
+        )
+        print('Loading VIT Done')
+        print('Loading Q-Former')
+        self.Qformer, self.query_tokens = self.init_Qformer(
+            num_query_token, self.visual_encoder.num_features
+        )
+        self.Qformer.cls = None
+        self.Qformer.bert.embeddings.word_embeddings = None
+        self.Qformer.bert.embeddings.position_embeddings = None
+        for layer in self.Qformer.bert.encoder.layer:
+            layer.output = None
+            layer.intermediate = None
+        self.load_from_pretrained(url_or_filename=q_former_model)
+        print('Loading Q-Former Done')
+        self.llama_proj = nn.Linear(
+            self.Qformer.config.hidden_size, 5120 # self.llama_model.config.hidden_size
+        )
+        self.max_txt_len = max_txt_len
+        self.end_sym = end_sym
+        self.model = MyModel(["main", *args])
+        # system promt
+        self.model.eval_string("Give the following image: <Img>ImageContent</Img>. "
+           "You will be able to see the image once I provide it to you. Please answer my questions."
+           "###")
+
+    def encode_img(self, image):
+        image = self.preprocessor(image)
+        image = image.unsqueeze(0)
+        device = image.device
+        if self.low_resource:
+            self.vit_to_cpu()
+            image = image.to("cpu")
+
+        with self.maybe_autocast():
+            image_embeds = self.ln_vision(self.visual_encoder(image)).to(device)
+            image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(device)
+
+            query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
+            query_output = self.Qformer.bert(
+                query_embeds=query_tokens,
+                encoder_hidden_states=image_embeds,
+                encoder_attention_mask=image_atts,
+                return_dict=True,
+            )
+
+            inputs_llama = self.llama_proj(query_output.last_hidden_state)
+            # atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(image.device)
+        return inputs_llama
+
+    def load_projection(self, path):
+        state = torch.load(path)["model"]
+        self.llama_proj.load_state_dict({
+            "weight": state["llama_proj.weight"],
+            "bias": state["llama_proj.bias"]})
+
+    def chat(self, question):
+        self.model.eval_string("Human: ")
+        self.model.eval_string(question)
+        self.model.eval_string("\n### Assistant:")
+        return self.model.generate_with_print(end="###")
+
+    def chat_with_image(self, image, question):
+        with torch.no_grad():
+            embd_image = self.encode_img(image)
+        embd_image = embd_image.cpu().numpy()[0]
+        self.model.eval_string("Human: <Img>")
+        self.model.eval_float(embd_image.T)
+        self.model.eval_string("</Img> ")
+        self.model.eval_string(question)
+        self.model.eval_string("\n### Assistant:")
+        return self.model.generate_with_print(end="###")
+
+
+if __name__=="__main__":
+    a = MiniGPT4(["--model", "./models/ggml-vicuna-13b-v0-q4_1.bin", "-c", "2048"])
+    a.load_projection(os.path.join(
+        os.path.dirname(__file__) ,
+        "pretrained_minigpt4.pth"))
+    respose = a.chat_with_image(
+        Image.open("./media/llama1-logo.png").convert('RGB'),
+        "what is the text in the picture?")
+    a.chat("what is the color of it?")